Learning belief connections in a model for situation awareness

Maria L. Gini, Mark Hoogendoorn, Rianne Van Lambalgen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Situational awareness is critical in many human tasks, especially in cases where humans have to make decisions fast and where the result of their decisions might affect their life. This paper addresses the problem of learning optimal values for the parameters of a situational awareness model. The model is a complex network with nodes connected by links with weights, which connect observations to simple beliefs, such as "there is a contact", to complex belief, such as "the contact is hostile", and to future beliefs, such as "it is possible the pilot is being targeted". The model has been built and validated by human experts in the domain of F16 fighter pilots and is used to study human decision making. Given the complexity of the model, there is a need to learn appropriate weights for the connections, which, in turn, affect the activation levels of the beliefs. We propose the use of a genetic algorithm and of a sensitivity based approach to learn the weights in the model. Extensive experimental results are included.

Original languageEnglish (US)
Title of host publicationAgents in Principle, Agents in Practice - 14th International Conference, PRIMA 2011, Proceedings
Number of pages12
StatePublished - 2011
Event14th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2011 - Wollongong, NSW, Australia
Duration: Nov 16 2011Nov 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7047 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2011
CityWollongong, NSW


Dive into the research topics of 'Learning belief connections in a model for situation awareness'. Together they form a unique fingerprint.

Cite this